Collecting data in market research is the key to finding insights that will help brands understand their products and services better and help them innovate and have a competitive advantage.
So what is data synthesis? Well, data synthesis, also known as the literature review or joining the dots, is a source of data collection in research where a researcher collects all the data and reviews it according to the relevance of the research.
Data synthesis is basically a process of collecting data from the set of included studies that aim to draw conclusions about the body of evidence that includes the study of characters and potentially helps with the stats of the study. When you collect synthetic data for a study, you assess how many previous studies on the topic have been conducted, and you collect data from all of those studies and relate it to your current study to form statistics and insights that will help you in research. For example, if you have to do a search on the consumption of social media in Gen-Z, you can go on the deep search engines and find data related to your topic, right from articles, research papers, and everything in between, giving you a rough idea of market research. With synthesis data, you combine different evidence points, which are often these metrics from qualitative and quantitative research and from different paradigms. Connecting the dots is difficult because different sources can appear incomparable, especially when they are not designed to work together, but as a good researcher, you will find a way around it.
Meta-analysis in market research is a new way to study data. Unlike traditional literature reviews, meta-analysis focuses on studying data from primary research and gathering data statistics from it. You can do a meta-analysis when there are multiple scientific studies that are addressing the same question with respect to your study. The meta-analysis studies are weighted by precision, which is derived from the standard error of individual studies, whereas the larger studies come in with a larger sample size. The term “inverse variance” is often used to describe this weighting mechanism. The results might be summarized meta-analytically by graphs and forest plots.